Noise characterization in a stochastic neural communication network

Recent advances in nanotechnology lead to designs for a new generation of communication systems using nano-scale elements. Researchers in nanocommunication networks propose novel engineering solutions for various application areas such as biological neural systems. In a neural communication network, the signals are encoded, propagated through synaptic channels, and decoded in a noise-free network. The desired performance of the nanocommunication network would be influenced by either internal or external disturbances, i.e. the spiking irregularities, history of firing in the neural cell, and the randomness in release of the neurotransmitters depending on the operating conditions. In this paper, a noise-free biological communication network is stochastically modeled. The internal and external disturbance sources are characterized considering the in-body communications and a comprehensive stochastic model is developed to verify the effects of various noise sources. The proposed model is comprised of a signal dependent encoding noise and signal independent synaptic/ionic disturbances. An effective probabilistic algorithm is given to model the firing rate of the neurons, while the noise sources are coupled. The proposed model is numerically studied and simulated, when various noise sources are applied simultaneously on the neural communication network.